PeopleofSuperintelligence

Researcher

Amanda Coston

Amanda Coston is a statistician at UC Berkeley who studies the reliability and fairness of algorithms used in decisions about people. She graduated from Princeton in 2013 and worked at Microsoft, Teneo and the Nairobi-based startup Hivisasa before completing a doctorate in machine learning and public policy at Carnegie Mellon in 2023. Her research developed ways to assess risk predictions under possible interventions and examine fairness across models with similar predictive performance. A 2023 paper with Luke Guerdan, Kenneth Holstein and Zhiwei Steven Wu won a FAccT Best Paper Award for work on measurement error and selection bias. She joined Berkeley’s Department of Statistics in 2024 after a postdoc at Microsoft Research New England.

Works
33
Citations
522
h-index
11
Badges

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2013–2019

    Princeton and early industry work

    She earns a BSE in computer science at Princeton in 2013, with a certificate in public policy and Robert Schapire as her advisor. After graduation, she works as a program manager at Microsoft, then as a data scientist at Teneo and the Nairobi-based startup Hivisasa. In 2019, she earns a master’s degree at Carnegie Mellon University.

  2. 2019

    Counterfactual risk assessments

    In 2019, Coston coauthors Counterfactual Risk Assessments, Evaluation, and Fairness with Alan Mishler, Edward H. Kennedy and Alexandra Chouldechova. The paper develops measures of predictive performance and fairness for decisions that can change outcomes, and tests them using simulated data and a child-welfare dataset.

  3. 2021

    Fairness across predictive models

    At the International Conference on Machine Learning in 2021, she coauthors Characterizing Fairness Over the Set of Good Models Under Selective Labels with Ashesh Rambachan and Alexandra Chouldechova. Their framework examines fairness across models with similar overall performance and addresses data in which outcomes are observed for selected cases; they apply it to credit scoring and recidivism prediction.

  4. 2023

    Doctorate at Carnegie Mellon

    Coston completes her PhD in machine learning and public policy at Carnegie Mellon University in 2023, advised by Alexandra Chouldechova and Edward H. Kennedy. Her doctoral research uses causal inference and machine learning to make algorithmic decisions more reliable and equitable.

  5. June 2023

    A FAccT paper award

    In June 2023, she shares a Best Paper Award at the ACM Conference on Fairness, Accountability, and Transparency for Counterfactual Prediction Under Outcome Measurement Error, coauthored with Luke Guerdan, Kenneth Holstein and Zhiwei Steven Wu. The team develops a method that accounts for errors in outcome measurement, treatment effects and selection bias when training predictive models.

  6. July 2023

    Postdoc at Microsoft Research

    She starts a postdoc at Microsoft Research New England in July 2023, joining its Machine Learning and Statistics Team.

  7. 2023

    An AI2050 fellowship

    She joins the 2023 cohort of Schmidt Sciences AI2050 Early Career Fellows, announced in March 2024. The fellowship supports a two-year research project on challenges and opportunities in artificial intelligence.

  8. Since 2024

    Professor at Berkeley

    In 2024, she joins UC Berkeley’s Department of Statistics as an assistant professor, where she studies the reliability and equity of algorithmic decision support using causal inference and machine learning. In 2026, she coauthors Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias with Annie Ulichney, developing a method to assess predictive models when the population changes and outcomes are observed for selected cases.

Career

  1. Since 2024University of California, BerkeleyAssistant Professor of Statistics
  2. 2024MicrosoftProgram Manager
  3. From 2023Microsoft ResearchPostdoctoral Researcher
  4. TeneoData Scientist
  5. HivisasaData Scientist

Education

  1. 2023Carnegie Mellon UniversityPhD in Machine Learning and Public Policy
  2. 2019Carnegie Mellon UniversityMaster's Degree
  3. 2013Princeton UniversityBSE in Computer Science

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Amanda Coston yet. Send us a free-licensed photo.

  1. Meet UC Berkeley’s new faculty news.berkeley.edu. Checked 27 October 2026.
  2. New Faculty: Amanda Coston, Assistant Professor statistics.berkeley.edu. Checked 27 October 2026.
  3. Amanda Coston – RegLab reglab.stanford.edu. Checked 27 October 2026.
  4. Counterfactual Risk Assessments, Evaluation, and Fairness arxiv.org. Checked 27 October 2026.
  5. Characterizing Fairness Over the Set of Good Models Under Selective Labels proceedings.mlr.press. Checked 27 October 2026.
  6. Amanda Coston mandycoston.github.io. Checked 27 October 2026.
  7. Counterfactual Prediction Under Outcome Measurement Error arxiv.org. Checked 27 October 2026.
  8. Second Cohort of AI2050 Early Career Fellows Named By Schmidt Sciences schmidtsciences.org. Checked 27 October 2026.
  9. CDSS welcomes seven faculty, celebrates two new college chairs cdss.berkeley.edu. Checked 27 October 2026.
  10. Amanda Coston | Department of Statistics statistics.berkeley.edu. Checked 27 October 2026.
  11. Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias arxiv.org. Checked 27 October 2026.